Bayesian Average

Jelte Hoekstra has a fun post applying the Bayesian average to board game ratings:

Maybe you want to explore the best boardgames but instead you find the top 100 filled with 10/10 scores. Experience many such false positives and you will lose faith in the rating system. Let’s be clear this isn’t exactly incidental either: most games have relatively few votes and suffer from this phenomenon.

The Bayesian average

Fortunately, there are ways to deal with this. BoardGameGeek’s solution is to replace the average by the Bayesian average. In Bayesian statistics we start out with a prior that represents our a priori assumptions. When evidence comes in we can update this prior, computing a so called posterior that reflects our updated belief.

Applied to boardgames this means: if we have an unrated game we might as well assume it’s average. If not, the ratings will have to convince us otherwise. This certainly removes outliers as we will see below!

This is a rather interesting article and you can easily apply it to other rating systems as well.

Related Posts

Random Forests In R

Anish Sing Walia explains the basics of random forests and provides sample code in R: Random Forests are similar to a famous Ensemble technique called Bagging but have a different tweak in it. In Random Forests the idea is to decorrelate the several trees which are generated on the different bootstrapped samples from training Data.And […]

Read More

Using seplyr Instead Of dplyr

John Mount explains seplyr and why it can be better for certain use cases than dplyr: seplyr is a dplyr adapter layer that prefers “slightly clunkier” standard interfaces (or referentially transparent interfaces), which are actually very powerful and can be used to some advantage. The above description and comparisons can come off as needlessly broad and painfully abstract. Things are […]

Read More


June 2017
« May Jul »